Overview

Dataset statistics

Number of variables19
Number of observations224
Missing cells4
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory33.4 KiB
Average record size in memory152.6 B

Variable types

Categorical4
Numeric15

Alerts

Video has a high cardinality: 224 distinct values High cardinality
Video title has a high cardinality: 223 distinct values High cardinality
Video pub­lish time has a high cardinality: 222 distinct values High cardinality
Av­er­age view dur­a­tion has a high cardinality: 158 distinct values High cardinality
Com­ments ad­ded is highly correlated with Shares and 9 other fieldsHigh correlation
Shares is highly correlated with Com­ments ad­ded and 10 other fieldsHigh correlation
Dis­likes is highly correlated with Com­ments ad­ded and 9 other fieldsHigh correlation
Likes is highly correlated with Com­ments ad­ded and 10 other fieldsHigh correlation
Sub­scribers lost is highly correlated with Com­ments ad­ded and 8 other fieldsHigh correlation
Sub­scribers gained is highly correlated with Com­ments ad­ded and 10 other fieldsHigh correlation
RPM (USD) is highly correlated with CPM (USD) and 1 other fieldsHigh correlation
CPM (USD) is highly correlated with RPM (USD)High correlation
Views is highly correlated with Com­ments ad­ded and 10 other fieldsHigh correlation
Watch time (hours) is highly correlated with Com­ments ad­ded and 10 other fieldsHigh correlation
Sub­scribers is highly correlated with Com­ments ad­ded and 9 other fieldsHigh correlation
Your es­tim­ated rev­en­ue (USD) is highly correlated with Com­ments ad­ded and 11 other fieldsHigh correlation
Im­pres­sions is highly correlated with Com­ments ad­ded and 9 other fieldsHigh correlation
Im­pres­sions click-through rate (%) is highly correlated with Shares and 6 other fieldsHigh correlation
Com­ments ad­ded is highly correlated with Shares and 9 other fieldsHigh correlation
Shares is highly correlated with Com­ments ad­ded and 9 other fieldsHigh correlation
Dis­likes is highly correlated with Com­ments ad­ded and 9 other fieldsHigh correlation
Likes is highly correlated with Com­ments ad­ded and 9 other fieldsHigh correlation
Sub­scribers lost is highly correlated with Com­ments ad­ded and 9 other fieldsHigh correlation
Sub­scribers gained is highly correlated with Com­ments ad­ded and 9 other fieldsHigh correlation
Views is highly correlated with Com­ments ad­ded and 9 other fieldsHigh correlation
Watch time (hours) is highly correlated with Com­ments ad­ded and 9 other fieldsHigh correlation
Sub­scribers is highly correlated with Com­ments ad­ded and 9 other fieldsHigh correlation
Your es­tim­ated rev­en­ue (USD) is highly correlated with Com­ments ad­ded and 9 other fieldsHigh correlation
Im­pres­sions is highly correlated with Com­ments ad­ded and 9 other fieldsHigh correlation
Com­ments ad­ded is highly correlated with Shares and 6 other fieldsHigh correlation
Shares is highly correlated with Com­ments ad­ded and 8 other fieldsHigh correlation
Dis­likes is highly correlated with Shares and 6 other fieldsHigh correlation
Likes is highly correlated with Com­ments ad­ded and 8 other fieldsHigh correlation
Sub­scribers lost is highly correlated with Com­ments ad­dedHigh correlation
Sub­scribers gained is highly correlated with Shares and 7 other fieldsHigh correlation
Views is highly correlated with Com­ments ad­ded and 8 other fieldsHigh correlation
Watch time (hours) is highly correlated with Com­ments ad­ded and 7 other fieldsHigh correlation
Sub­scribers is highly correlated with Shares and 7 other fieldsHigh correlation
Your es­tim­ated rev­en­ue (USD) is highly correlated with Com­ments ad­ded and 8 other fieldsHigh correlation
Im­pres­sions is highly correlated with Com­ments ad­ded and 8 other fieldsHigh correlation
Com­ments ad­ded is highly correlated with Shares and 9 other fieldsHigh correlation
Shares is highly correlated with Com­ments ad­ded and 9 other fieldsHigh correlation
Dis­likes is highly correlated with Com­ments ad­ded and 9 other fieldsHigh correlation
Likes is highly correlated with Com­ments ad­ded and 9 other fieldsHigh correlation
Sub­scribers lost is highly correlated with Com­ments ad­ded and 9 other fieldsHigh correlation
Sub­scribers gained is highly correlated with Com­ments ad­ded and 9 other fieldsHigh correlation
RPM (USD) is highly correlated with Av­er­age per­cent­age viewed (%)High correlation
Av­er­age per­cent­age viewed (%) is highly correlated with RPM (USD)High correlation
Views is highly correlated with Com­ments ad­ded and 9 other fieldsHigh correlation
Watch time (hours) is highly correlated with Com­ments ad­ded and 9 other fieldsHigh correlation
Sub­scribers is highly correlated with Com­ments ad­ded and 9 other fieldsHigh correlation
Your es­tim­ated rev­en­ue (USD) is highly correlated with Com­ments ad­ded and 9 other fieldsHigh correlation
Im­pres­sions is highly correlated with Com­ments ad­ded and 9 other fieldsHigh correlation
Video is uniformly distributed Uniform
Video title is uniformly distributed Uniform
Video pub­lish time is uniformly distributed Uniform
Av­er­age view dur­a­tion is uniformly distributed Uniform
Video has unique values Unique
Watch time (hours) has unique values Unique
Your es­tim­ated rev­en­ue (USD) has unique values Unique
Im­pres­sions has unique values Unique
Dis­likes has 28 (12.5%) zeros Zeros
Sub­scribers lost has 23 (10.3%) zeros Zeros
Sub­scribers has 3 (1.3%) zeros Zeros

Reproduction

Analysis started2022-04-04 00:56:47.913804
Analysis finished2022-04-04 00:57:28.477201
Duration40.56 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Video
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct224
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
Total
 
1
4OZip0cgOho
 
1
UuR3nomI5AE
 
1
akbU9KOo_Qc
 
1
KHAuuOQui2U
 
1
Other values (219)
219 

Length

Max length12
Median length11
Mean length10.99107143
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique224 ?
Unique (%)100.0%

Sample

1st rowTotal
2nd row4OZip0cgOho
3rd row78LjdAAw0wA
4th rowhO_YKK_0Qck
5th rowuXLnbdHMf8w

Common Values

ValueCountFrequency (%)
Total1
 
0.4%
4OZip0cgOho1
 
0.4%
UuR3nomI5AE1
 
0.4%
akbU9KOo_Qc1
 
0.4%
KHAuuOQui2U1
 
0.4%
dlZWB2D-NaQ1
 
0.4%
iiSZqsQKNX81
 
0.4%
n6MiRgxN5iA1
 
0.4%
omdhf8d53FM1
 
0.4%
B1g_yMKpdwo1
 
0.4%
Other values (214)214
95.5%

Length

2022-04-04T00:57:28.585805image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
total1
 
0.4%
4ozip0cgoho1
 
0.4%
8igh8qzafpo1
 
0.4%
78ljdaaw0wa1
 
0.4%
ho_ykk_0qck1
 
0.4%
uxlnbdhmf8w1
 
0.4%
xgg7dikys9e1
 
0.4%
3d1nctsv0c1
 
0.4%
ip50cxvpwy41
 
0.4%
4qzinlzwyyk1
 
0.4%
Other values (214)214
95.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Video title
Categorical

HIGH CARDINALITY
UNIFORM

Distinct223
Distinct (%)100.0%
Missing1
Missing (%)0.4%
Memory size1.9 KiB
How I Would Learn Data Science (If I Had to Start Over)
 
1
Reviewing Your Data Science Projects - Episode 14 [Deep Learning Focus]
 
1
How to Stay Productive & Motivated When Learning Data Science
 
1
What You Need to Know for a Data Science Internship
 
1
The 4 Types of Sports Analytics Projects
 
1
Other values (218)
218 

Length

Max length100
Median length55
Mean length54.24215247
Min length21

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique223 ?
Unique (%)100.0%

Sample

1st rowHow I Would Learn Data Science (If I Had to Start Over)
2nd row100K Channel Update + AMA Stream!
3rd rowUber Driver to Machine Learning Engineer in 9 Months! (@Daniel Bourke) - KNN EP. 05
4th rowWhy I'm Starting Data Science Over Again.
5th rowInterview with the Director of AI Research @ NVIDIA (Anima Anandkumar) - KNN EP. 07

Common Values

ValueCountFrequency (%)
How I Would Learn Data Science (If I Had to Start Over)1
 
0.4%
Reviewing Your Data Science Projects - Episode 14 [Deep Learning Focus]1
 
0.4%
How to Stay Productive & Motivated When Learning Data Science1
 
0.4%
What You Need to Know for a Data Science Internship1
 
0.4%
The 4 Types of Sports Analytics Projects1
 
0.4%
#66DaysOfData - What is it? #shorts1
 
0.4%
Why EVERYONE Should Start a Podcast (Including YOU)1
 
0.4%
Land a Data Science Job in a Different Country (Vijay Pravin Maharajan) - KNN EP. 131
 
0.4%
Reviewing Your Data Science Projects - Episode 10 (Leveraging Your Data)1
 
0.4%
What is a lambda function (python)? #shorts1
 
0.4%
Other values (213)213
95.1%

Length

2022-04-04T00:57:28.980324image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
data190
 
9.2%
science145
 
7.0%
88
 
4.2%
a48
 
2.3%
the47
 
2.3%
to46
 
2.2%
how37
 
1.8%
your33
 
1.6%
projects29
 
1.4%
for27
 
1.3%
Other values (610)1385
66.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Video pub­lish time
Categorical

HIGH CARDINALITY
UNIFORM

Distinct222
Distinct (%)99.6%
Missing1
Missing (%)0.4%
Memory size1.9 KiB
Mar 3, 2019
 
2
Dec 13, 2019
 
1
Aug 3, 2020
 
1
Sep 29, 2019
 
1
May 1, 2020
 
1
Other values (217)
217 

Length

Max length12
Median length12
Mean length11.71748879
Min length11

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique221 ?
Unique (%)99.1%

Sample

1st rowMay 8, 2020
2nd rowNov 12, 2020
3rd rowJul 16, 2020
4th rowAug 29, 2020
5th rowAug 5, 2020

Common Values

ValueCountFrequency (%)
Mar 3, 20192
 
0.9%
Dec 13, 20191
 
0.4%
Aug 3, 20201
 
0.4%
Sep 29, 20191
 
0.4%
May 1, 20201
 
0.4%
Feb 3, 20201
 
0.4%
Jun 11, 20211
 
0.4%
Nov 5, 20211
 
0.4%
Sep 16, 20201
 
0.4%
Jun 29, 20201
 
0.4%
Other values (212)212
94.6%

Length

2022-04-04T00:57:29.190427image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020113
 
16.9%
201950
 
7.5%
202147
 
7.0%
jul24
 
3.6%
aug21
 
3.1%
mar20
 
3.0%
apr19
 
2.8%
dec19
 
2.8%
nov19
 
2.8%
may19
 
2.8%
Other values (39)318
47.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Com­ments ad­ded
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct111
Distinct (%)49.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.75
Minimum0
Maximum14197
Zeros1
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2022-04-04T00:57:29.380430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q118
median37
Q366.25
95-th percentile237.95
Maximum14197
Range14197
Interquartile range (IQR)48.25

Descriptive statistics

Standard deviation948.7323712
Coefficient of variation (CV)7.485068017
Kurtosis219.8116327
Mean126.75
Median Absolute Deviation (MAD)23
Skewness14.76133139
Sum28392
Variance900093.1121
MonotonicityDecreasing
2022-04-04T00:57:29.588607image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
711
 
4.9%
147
 
3.1%
375
 
2.2%
275
 
2.2%
225
 
2.2%
135
 
2.2%
584
 
1.8%
714
 
1.8%
244
 
1.8%
294
 
1.8%
Other values (101)170
75.9%
ValueCountFrequency (%)
01
 
0.4%
21
 
0.4%
33
 
1.3%
44
 
1.8%
51
 
0.4%
64
 
1.8%
711
4.9%
82
 
0.9%
94
 
1.8%
102
 
0.9%
ValueCountFrequency (%)
141971
0.4%
9071
0.4%
4121
0.4%
4021
0.4%
3751
0.4%
3291
0.4%
3171
0.4%
2941
0.4%
2671
0.4%
2461
0.4%

Shares
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct136
Distinct (%)60.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean353.9241071
Minimum0
Maximum39640
Zeros1
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2022-04-04T00:57:29.804404image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q119
median46.5
Q3114.25
95-th percentile536.95
Maximum39640
Range39640
Interquartile range (IQR)95.25

Descriptive statistics

Standard deviation2736.321694
Coefficient of variation (CV)7.731379803
Kurtosis193.3193008
Mean353.9241071
Median Absolute Deviation (MAD)34.5
Skewness13.58227001
Sum79279
Variance7487456.411
MonotonicityNot monotonic
2022-04-04T00:57:30.024243image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
57
 
3.1%
117
 
3.1%
86
 
2.7%
395
 
2.2%
495
 
2.2%
124
 
1.8%
24
 
1.8%
604
 
1.8%
344
 
1.8%
424
 
1.8%
Other values (126)174
77.7%
ValueCountFrequency (%)
01
 
0.4%
13
1.3%
24
1.8%
32
 
0.9%
43
1.3%
57
3.1%
62
 
0.9%
72
 
0.9%
86
2.7%
91
 
0.4%
ValueCountFrequency (%)
396401
0.4%
95831
0.4%
46941
0.4%
19351
0.4%
12651
0.4%
8681
0.4%
7671
0.4%
7441
0.4%
7411
0.4%
5841
0.4%

Dis­likes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct50
Distinct (%)22.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.83928571
Minimum0
Maximum3902
Zeros28
Zeros (%)12.5%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2022-04-04T00:57:30.248181image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q311
95-th percentile63.85
Maximum3902
Range3902
Interquartile range (IQR)10

Descriptive statistics

Standard deviation268.6683171
Coefficient of variation (CV)7.711648263
Kurtosis195.2965213
Mean34.83928571
Median Absolute Deviation (MAD)3
Skewness13.67120234
Sum7804
Variance72182.66464
MonotonicityNot monotonic
2022-04-04T00:57:30.484201image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
232
14.3%
130
13.4%
028
12.5%
415
 
6.7%
315
 
6.7%
512
 
5.4%
69
 
4.0%
88
 
3.6%
116
 
2.7%
136
 
2.7%
Other values (40)63
28.1%
ValueCountFrequency (%)
028
12.5%
130
13.4%
232
14.3%
315
6.7%
415
6.7%
512
 
5.4%
69
 
4.0%
75
 
2.2%
88
 
3.6%
96
 
2.7%
ValueCountFrequency (%)
39021
0.4%
9421
0.4%
2531
0.4%
2001
0.4%
1841
0.4%
1591
0.4%
1291
0.4%
1001
0.4%
931
0.4%
871
0.4%

Likes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct201
Distinct (%)89.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2008.919643
Minimum1
Maximum225021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2022-04-04T00:57:30.709800image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile44.6
Q1163.5
median342.5
Q3716
95-th percentile2782.25
Maximum225021
Range225020
Interquartile range (IQR)552.5

Descriptive statistics

Standard deviation15387.27401
Coefficient of variation (CV)7.659477107
Kurtosis200.560756
Mean2008.919643
Median Absolute Deviation (MAD)224
Skewness13.89547843
Sum449998
Variance236768201.6
MonotonicityNot monotonic
2022-04-04T00:57:30.923343image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1313
 
1.3%
1683
 
1.3%
422
 
0.9%
2972
 
0.9%
4502
 
0.9%
2732
 
0.9%
2472
 
0.9%
6912
 
0.9%
1572
 
0.9%
242
 
0.9%
Other values (191)202
90.2%
ValueCountFrequency (%)
11
0.4%
181
0.4%
242
0.9%
281
0.4%
301
0.4%
311
0.4%
351
0.4%
381
0.4%
422
0.9%
441
0.4%
ValueCountFrequency (%)
2250211
0.4%
469031
0.4%
194641
0.4%
147081
0.4%
58691
0.4%
52181
0.4%
44131
0.4%
43211
0.4%
35301
0.4%
32251
0.4%

Sub­scribers lost
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct43
Distinct (%)19.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean217.6205357
Minimum0
Maximum45790
Zeros23
Zeros (%)10.3%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2022-04-04T00:57:31.119117image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median7
Q314
95-th percentile44.25
Maximum45790
Range45790
Interquartile range (IQR)12

Descriptive statistics

Standard deviation3058.766918
Coefficient of variation (CV)14.0555068
Kurtosis223.9466141
Mean217.6205357
Median Absolute Deviation (MAD)5
Skewness14.96397989
Sum48747
Variance9356055.062
MonotonicityNot monotonic
2022-04-04T00:57:31.317246image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
023
 
10.3%
221
 
9.4%
116
 
7.1%
1015
 
6.7%
614
 
6.2%
512
 
5.4%
911
 
4.9%
311
 
4.9%
411
 
4.9%
118
 
3.6%
Other values (33)82
36.6%
ValueCountFrequency (%)
023
10.3%
116
7.1%
221
9.4%
311
4.9%
411
4.9%
512
5.4%
614
6.2%
78
 
3.6%
86
 
2.7%
911
4.9%
ValueCountFrequency (%)
457901
0.4%
4511
0.4%
1311
0.4%
851
0.4%
721
0.4%
671
0.4%
571
0.4%
532
0.9%
511
0.4%
461
0.4%

Sub­scribers gained
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct163
Distinct (%)72.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1608.084821
Minimum0
Maximum229241
Zeros1
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2022-04-04T00:57:31.514845image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.15
Q127
median70
Q3245.5
95-th percentile2129.1
Maximum229241
Range229241
Interquartile range (IQR)218.5

Descriptive statistics

Standard deviation15628.83411
Coefficient of variation (CV)9.718911532
Kurtosis204.5923297
Mean1608.084821
Median Absolute Deviation (MAD)57.5
Skewness14.09112765
Sum360211
Variance244260455.8
MonotonicityNot monotonic
2022-04-04T00:57:31.720325image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
195
 
2.2%
1424
 
1.8%
124
 
1.8%
1324
 
1.8%
204
 
1.8%
154
 
1.8%
294
 
1.8%
443
 
1.3%
213
 
1.3%
63
 
1.3%
Other values (153)186
83.0%
ValueCountFrequency (%)
01
 
0.4%
11
 
0.4%
22
0.9%
31
 
0.4%
42
0.9%
52
0.9%
63
1.3%
72
0.9%
81
 
0.4%
92
0.9%
ValueCountFrequency (%)
2292411
0.4%
469041
0.4%
107341
0.4%
95081
0.4%
33581
0.4%
31841
0.4%
27931
0.4%
25851
0.4%
25531
0.4%
23951
0.4%

RPM (USD)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct219
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.442040179
Minimum0
Maximum10.387
Zeros1
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2022-04-04T00:57:31.926137image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.8565
Q13.22075
median4.3345
Q35.37225
95-th percentile7.5629
Maximum10.387
Range10.387
Interquartile range (IQR)2.1515

Descriptive statistics

Standard deviation1.789147938
Coefficient of variation (CV)0.4027761718
Kurtosis0.7007870808
Mean4.442040179
Median Absolute Deviation (MAD)1.096
Skewness0.5796672481
Sum995.017
Variance3.201050344
MonotonicityNot monotonic
2022-04-04T00:57:32.149345image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.1252
 
0.9%
4.2472
 
0.9%
4.6522
 
0.9%
3.2172
 
0.9%
4.3342
 
0.9%
5.2761
 
0.4%
4.3181
 
0.4%
4.6481
 
0.4%
5.5461
 
0.4%
2.9681
 
0.4%
Other values (209)209
93.3%
ValueCountFrequency (%)
01
0.4%
0.051
0.4%
0.5421
0.4%
1.0391
0.4%
1.3261
0.4%
1.3781
0.4%
1.6081
0.4%
1.6981
0.4%
1.6991
0.4%
1.8061
0.4%
ValueCountFrequency (%)
10.3871
0.4%
9.961
0.4%
9.5161
0.4%
9.4771
0.4%
8.851
0.4%
8.6471
0.4%
8.3281
0.4%
8.3151
0.4%
8.0271
0.4%
7.9211
0.4%

CPM (USD)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct217
Distinct (%)97.7%
Missing2
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean11.44277928
Minimum5.439
Maximum37.786
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2022-04-04T00:57:32.364433image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum5.439
5-th percentile6.99405
Q19.3795
median11.1695
Q312.901
95-th percentile16.18485
Maximum37.786
Range32.347
Interquartile range (IQR)3.5215

Descriptive statistics

Standard deviation3.334780901
Coefficient of variation (CV)0.2914310256
Kurtosis17.00443894
Mean11.44277928
Median Absolute Deviation (MAD)1.775
Skewness2.493988209
Sum2540.297
Variance11.12076366
MonotonicityNot monotonic
2022-04-04T00:57:32.736039image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.8052
 
0.9%
11.3022
 
0.9%
7.5712
 
0.9%
14.952
 
0.9%
12.592
 
0.9%
9.5911
 
0.4%
16.8911
 
0.4%
8.8651
 
0.4%
6.1581
 
0.4%
14.9811
 
0.4%
Other values (207)207
92.4%
(Missing)2
 
0.9%
ValueCountFrequency (%)
5.4391
0.4%
5.7861
0.4%
5.9811
0.4%
6.0321
0.4%
6.1581
0.4%
6.2191
0.4%
6.2591
0.4%
6.3361
0.4%
6.5221
0.4%
6.7641
0.4%
ValueCountFrequency (%)
37.7861
0.4%
22.6781
0.4%
19.3221
0.4%
19.0931
0.4%
18.4091
0.4%
18.0561
0.4%
17.5431
0.4%
17.4171
0.4%
16.9221
0.4%
16.8911
0.4%

Av­er­age per­cent­age viewed (%)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct220
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.08727679
Minimum5.23
Maximum76.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2022-04-04T00:57:32.944361image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum5.23
5-th percentile10.332
Q123.6025
median35.12
Q343.4625
95-th percentile58.6125
Maximum76.6
Range71.37
Interquartile range (IQR)19.86

Descriptive statistics

Standard deviation15.11874613
Coefficient of variation (CV)0.4435304769
Kurtosis-0.1775551953
Mean34.08727679
Median Absolute Deviation (MAD)9.945
Skewness0.2509374927
Sum7635.55
Variance228.5764845
MonotonicityNot monotonic
2022-04-04T00:57:33.159924image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.982
 
0.9%
48.632
 
0.9%
41.962
 
0.9%
34.982
 
0.9%
10.561
 
0.4%
36.251
 
0.4%
54.021
 
0.4%
40.961
 
0.4%
68.11
 
0.4%
36.811
 
0.4%
Other values (210)210
93.8%
ValueCountFrequency (%)
5.231
0.4%
6.261
0.4%
7.321
0.4%
7.61
0.4%
7.831
0.4%
8.431
0.4%
8.491
0.4%
8.791
0.4%
9.521
0.4%
9.551
0.4%
ValueCountFrequency (%)
76.61
0.4%
75.621
0.4%
74.351
0.4%
71.961
0.4%
70.681
0.4%
68.11
0.4%
66.561
0.4%
63.661
0.4%
61.591
0.4%
60.781
0.4%

Av­er­age view dur­a­tion
Categorical

HIGH CARDINALITY
UNIFORM

Distinct158
Distinct (%)70.5%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
0:03:50
 
5
0:02:50
 
4
0:03:33
 
4
0:03:52
 
3
0:03:09
 
3
Other values (153)
205 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique109 ?
Unique (%)48.7%

Sample

1st row0:03:25
2nd row0:03:09
3rd row0:05:14
4th row0:10:21
5th row0:02:36

Common Values

ValueCountFrequency (%)
0:03:505
 
2.2%
0:02:504
 
1.8%
0:03:334
 
1.8%
0:03:523
 
1.3%
0:03:093
 
1.3%
0:02:163
 
1.3%
0:02:293
 
1.3%
0:02:373
 
1.3%
0:02:403
 
1.3%
0:02:463
 
1.3%
Other values (148)190
84.8%

Length

2022-04-04T00:57:33.349869image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0:03:505
 
2.2%
0:03:334
 
1.8%
0:02:504
 
1.8%
0:02:403
 
1.3%
0:02:513
 
1.3%
0:04:023
 
1.3%
0:02:463
 
1.3%
0:02:063
 
1.3%
0:02:373
 
1.3%
0:02:293
 
1.3%
Other values (148)190
84.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Views
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct221
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49716.45089
Minimum60
Maximum5568487
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2022-04-04T00:57:33.530779image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile993.15
Q13940
median8347.5
Q318368.75
95-th percentile91577.1
Maximum5568487
Range5568427
Interquartile range (IQR)14428.75

Descriptive statistics

Standard deviation381030.2178
Coefficient of variation (CV)7.664067144
Kurtosis200.1467569
Mean49716.45089
Median Absolute Deviation (MAD)5474
Skewness13.89157059
Sum11136485
Variance1.451840269 × 1011
MonotonicityNot monotonic
2022-04-04T00:57:33.747320image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27942
 
0.9%
36732
 
0.9%
36152
 
0.9%
107621
 
0.4%
74801
 
0.4%
44701
 
0.4%
96691
 
0.4%
123171
 
0.4%
179821
 
0.4%
61011
 
0.4%
Other values (211)211
94.2%
ValueCountFrequency (%)
601
0.4%
4551
0.4%
4821
0.4%
5061
0.4%
5481
0.4%
6121
0.4%
6641
0.4%
7211
0.4%
8161
0.4%
9591
0.4%
ValueCountFrequency (%)
55684871
0.4%
12535591
0.4%
2972221
0.4%
2374671
0.4%
1681831
0.4%
1316301
0.4%
1235151
0.4%
1081331
0.4%
1028541
0.4%
987771
0.4%

Watch time (hours)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct224
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2835.711522
Minimum1.0684
Maximum317602.3536
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2022-04-04T00:57:33.953116image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1.0684
5-th percentile29.113075
Q1177.037125
median397.8522
Q31145.6214
95-th percentile6191.97757
Maximum317602.3536
Range317601.2852
Interquartile range (IQR)968.584275

Descriptive statistics

Standard deviation21662.5669
Coefficient of variation (CV)7.639199801
Kurtosis202.640694
Mean2835.711522
Median Absolute Deviation (MAD)292.46685
Skewness13.9933704
Sum635199.381
Variance469266804.6
MonotonicityNot monotonic
2022-04-04T00:57:34.176100image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
317602.35361
 
0.4%
65850.70421
 
0.4%
285.84191
 
0.4%
654.23391
 
0.4%
571.06711
 
0.4%
868.65971
 
0.4%
54.23921
 
0.4%
134.98151
 
0.4%
259.92461
 
0.4%
261.50421
 
0.4%
Other values (214)214
95.5%
ValueCountFrequency (%)
1.06841
0.4%
6.30891
0.4%
9.61881
0.4%
16.69751
0.4%
19.27521
0.4%
21.20731
0.4%
22.5451
0.4%
22.63381
0.4%
25.93751
0.4%
26.43461
0.4%
ValueCountFrequency (%)
317602.35361
0.4%
65850.70421
0.4%
17039.65661
0.4%
10560.35971
0.4%
10465.04671
0.4%
9057.27521
0.4%
8050.70311
0.4%
7724.27351
0.4%
7153.74371
0.4%
7104.52621
0.4%

Sub­scribers
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct159
Distinct (%)71.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1390.464286
Minimum-21
Maximum183451
Zeros3
Zeros (%)1.3%
Negative4
Negative (%)1.8%
Memory size1.9 KiB
2022-04-04T00:57:34.390259image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-21
5-th percentile2
Q119.75
median62.5
Q3230.5
95-th percentile2105.3
Maximum183451
Range183472
Interquartile range (IQR)210.75

Descriptive statistics

Standard deviation12647.24693
Coefficient of variation (CV)9.095700664
Kurtosis195.5190275
Mean1390.464286
Median Absolute Deviation (MAD)55.5
Skewness13.70405176
Sum311464
Variance159952854.8
MonotonicityNot monotonic
2022-04-04T00:57:34.603813image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76
 
2.7%
136
 
2.7%
104
 
1.8%
1294
 
1.8%
14
 
1.8%
194
 
1.8%
44
 
1.8%
273
 
1.3%
03
 
1.3%
443
 
1.3%
Other values (149)183
81.7%
ValueCountFrequency (%)
-211
 
0.4%
-51
 
0.4%
-31
 
0.4%
-21
 
0.4%
03
1.3%
14
1.8%
23
1.3%
32
0.9%
44
1.8%
52
0.9%
ValueCountFrequency (%)
1834511
0.4%
464531
0.4%
106031
0.4%
94361
0.4%
33011
0.4%
31621
0.4%
27401
0.4%
25451
0.4%
25071
0.4%
23621
0.4%

Your es­tim­ated rev­en­ue (USD)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct224
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean259.537433
Minimum0
Maximum29068.652
Zeros1
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2022-04-04T00:57:34.814044image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.7538
Q112.20825
median32.5955
Q396.81475
95-th percentile495.9963
Maximum29068.652
Range29068.652
Interquartile range (IQR)84.6065

Descriptive statistics

Standard deviation2011.119492
Coefficient of variation (CV)7.748860998
Kurtosis191.9259355
Mean259.537433
Median Absolute Deviation (MAD)25.5105
Skewness13.55428649
Sum58136.385
Variance4044601.613
MonotonicityNot monotonic
2022-04-04T00:57:35.023698image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29068.6521
 
0.4%
7959.5331
 
0.4%
10.9651
 
0.4%
41.7111
 
0.4%
57.1491
 
0.4%
99.5871
 
0.4%
12.0091
 
0.4%
13.1181
 
0.4%
12.5111
 
0.4%
11.9451
 
0.4%
Other values (214)214
95.5%
ValueCountFrequency (%)
01
0.4%
0.0031
0.4%
0.2741
0.4%
1.0941
0.4%
1.7011
0.4%
1.721
0.4%
1.8491
0.4%
1.9951
0.4%
2.0041
0.4%
2.3761
0.4%
ValueCountFrequency (%)
29068.6521
0.4%
7959.5331
0.4%
1217.0461
0.4%
929.41
0.4%
894.7531
0.4%
885.5041
0.4%
767.4931
0.4%
699.5581
0.4%
601.6711
0.4%
552.9271
0.4%

Im­pres­sions
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct224
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean901357.2545
Minimum365
Maximum100954064
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2022-04-04T00:57:35.247123image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum365
5-th percentile34646.8
Q199471
median154192.5
Q3289488.5
95-th percentile1405230.75
Maximum100954064
Range100953699
Interquartile range (IQR)190017.5

Descriptive statistics

Standard deviation6967916.065
Coefficient of variation (CV)7.730470943
Kurtosis193.6318441
Mean901357.2545
Median Absolute Deviation (MAD)73871
Skewness13.62131715
Sum201904025
Variance4.855185428 × 1013
MonotonicityNot monotonic
2022-04-04T00:57:35.471602image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1009540641
 
0.4%
264987991
 
0.4%
1110951
 
0.4%
1023631
 
0.4%
1941971
 
0.4%
2311231
 
0.4%
978781
 
0.4%
1162191
 
0.4%
966271
 
0.4%
1011511
 
0.4%
Other values (214)214
95.5%
ValueCountFrequency (%)
3651
0.4%
186351
0.4%
217801
0.4%
250941
0.4%
258851
0.4%
262021
0.4%
279601
0.4%
282381
0.4%
308101
0.4%
326501
0.4%
ValueCountFrequency (%)
1009540641
0.4%
264987991
0.4%
54470451
0.4%
45122541
0.4%
31376441
0.4%
29411301
0.4%
29273501
0.4%
22479121
0.4%
20405611
0.4%
17484671
0.4%

Im­pres­sions click-through rate (%)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct185
Distinct (%)82.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.084151786
Minimum0.49
Maximum11.51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2022-04-04T00:57:35.684128image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.49
5-th percentile0.85
Q11.925
median2.895
Q33.975
95-th percentile5.868
Maximum11.51
Range11.02
Interquartile range (IQR)2.05

Descriptive statistics

Standard deviation1.670448288
Coefficient of variation (CV)0.5416232417
Kurtosis2.854018415
Mean3.084151786
Median Absolute Deviation (MAD)1.03
Skewness1.182353729
Sum690.85
Variance2.790397484
MonotonicityNot monotonic
2022-04-04T00:57:35.903622image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.723
 
1.3%
4.013
 
1.3%
2.383
 
1.3%
2.513
 
1.3%
1.672
 
0.9%
3.012
 
0.9%
5.452
 
0.9%
0.872
 
0.9%
0.542
 
0.9%
4.362
 
0.9%
Other values (175)200
89.3%
ValueCountFrequency (%)
0.491
0.4%
0.511
0.4%
0.531
0.4%
0.542
0.9%
0.61
0.4%
0.641
0.4%
0.691
0.4%
0.721
0.4%
0.761
0.4%
0.81
0.4%
ValueCountFrequency (%)
11.511
0.4%
8.621
0.4%
8.41
0.4%
7.711
0.4%
7.451
0.4%
7.161
0.4%
6.61
0.4%
6.531
0.4%
6.181
0.4%
6.121
0.4%

Interactions

2022-04-04T00:57:24.840341image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:56:49.704592image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:56:52.357630image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:56:54.796140image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:56:57.435953image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:56:59.713062image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-04-04T00:57:19.282435image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:57:21.843156image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:57:24.533849image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:57:27.074379image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:56:52.197815image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:56:54.626320image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:56:57.274718image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:56:59.561085image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:57:02.113551image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:57:04.436084image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:57:06.962587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:57:09.294052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:57:11.944976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:57:14.459518image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:57:16.854533image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:57:19.442884image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:57:22.070772image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T00:57:24.686800image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-04-04T00:57:36.084678image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-04T00:57:36.522141image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-04T00:57:36.790905image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-04T00:57:37.067940image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-04-04T00:57:27.379673image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-04-04T00:57:27.885175image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-04-04T00:57:28.158716image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-04-04T00:57:28.272466image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

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0TotalNaNNaN14197396403902225021457902292415.27611.99026.610:03:255568487317602.353618345129068.6521009540643.16
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Last rows

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219FBgs-BSTIJEDemystifying Data Science RolesNov 30, 201835148185.21816.23255.550:03:2897856.593075.103262022.24
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